Cloud dfs super master detector location systems and methods

ABSTRACT

This application relates to wireless networks and more specifically to systems and methods for determining the location of distributed radar detectors and selecting available channels free of radar signals from a plurality of radio frequency channels. One embodiment includes a cloud DFS super master and a radar detector communicatively coupled to the cloud DFS super master. The cloud DFS super master is programmed to receive the results of the scan for a radar signal from the radar detector and to generate integrated client device geolocation information. The cloud DFS super master is also programmed to determine a location for the radar detector based at least on the integrated client device geolocation information, and determine a radio channel free of the radar signal based at least on the location for the radar detector and the results of the scan for the radar signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of, and claims priority to,U.S. patent application Ser. No. 15/214,437 titled “CLOUD DFS SUPERMASTER SYSTEMS AND METHODS” and filed on Jul. 19, 2016, which claimspriority to U.S. Provisional Patent Application No. 62/259,981 titled“CLOUD DFS SUPER MASTER SYSTEMS AND METHODS,” and filed on Nov. 25,2015, the disclosures of which are hereby incorporated herein byreference in their entirety.

BACKGROUND

The present disclosure relates to wireless networks and morespecifically to systems and methods for determining the location ofdistributed radar detectors and selecting available channels free ofradar signals from a plurality of radio frequency channels. Oneembodiment includes a cloud DFS super master and a radar detectorcommunicatively coupled to the cloud DFS super master. The cloud DFSsuper master is programmed to receive the results of the scan for aradar signal from the radar detector and to generate integrated clientdevice geolocation information. The cloud DFS super master is alsoprogrammed to determine a location for the radar detector based at leaston the integrated client device geolocation information, and determine aradio channel free of the radar signal based at least on the locationfor the radar detector and the results of the scan for the radar signal.

Wi-Fi networks are crucial to today's portable modern life. Wi-Fi is thepreferred network in the growing Internet-of-Things (IoT). But, thetechnology behind current Wi-Fi has changed little in the last tenyears. The Wi-Fi network and the associated unlicensed spectrum arecurrently managed in inefficient ways. For example, there is little orno coordination between individual networks and equipment from differentmanufacturers. Such networks generally employ primitive controlalgorithms that assume the network consists of “self-managed islands,” aconcept originally intended for low density and low trafficenvironments. The situation is far worse for home networks, which areassembled in completely chaotic ad hoc ways. Further, with more and moreconnected devices becoming commonplace, the net result is growingcongestion and slowed networks with unreliable connections.

Similarly, LTE-U networks operating in the same or similar unlicensedbands as 802.11 a/n/ac Wi-Fi suffer similar congestion and unreliableconnection issues and will often create congestion problems for existingWi-Fi networks sharing the same channels. Additional bandwidth andbetter and more efficient utilization of spectrum is key to sustainingthe usefulness of wireless networks including the Wi-Fi and LTE-Unetworks in a fast growing connected world.

Devices operating in certain parts of the 5 GHz U-NII-2 band, known asthe DFS bands or the DFS channels, require active radar detection. Thisfunction is assigned to a device capable of detecting radar known as aDFS master, which is typically an access point or router. The DFS masteractively scans the DFS channels and performs a channel availabilitycheck (CAC) and periodic in-service monitoring (ISM) after the channelavailability check. The channel availability check lasts 60 seconds asrequired by the Federal Communications Commission (FCC) Part 15 SubpartE and ETSI 301 893 standards. The DFS master signals to the otherdevices in the network (typically client devices) by transmitting a DFSbeacon indicating that the channel is clear of radar. Although theaccess point can detect radar, wireless clients typically cannot.Because of this, wireless clients must first passively scan DFS channelsto detect whether a beacon is present on that particular channel. Duringa passive scan, the client device switches through channels and listensfor a beacon transmitted at regular intervals by the access point on anavailable channel.

Once a beacon is detected, the client is allowed to transmit on thatchannel. If the DFS master detects radar in that channel, the DFS masterno longer transmits the beacon, and all client devices upon not sensingthe beacon within a prescribed time must vacate the channel immediatelyand remain off that channel for 30 minutes. For clients associated withthe DFS master network, additional information in the beacons (i.e. thechannel switch announcement) can trigger a rapid and controlledevacuation of the channel. Normally, a DFS master device is an accesspoint with only one radio and is able to provide DFS master services forjust a single channel. Significant problems of the current approachinclude: (1) hidden nodes; (2) hidden radar; (3) false radar detections;(4) long delays in DFS channel switching at radar detection or falseradar detection; (5) failure to support geo-fencing of areas of radaruse based on external data; (6) underutilization of the DFS spectrum dueto the dominance of private access points in the DFS spectrum; (7)interference between proximate LTE-U and Wi-Fi devices; and (8) lack ofspectrum-use coordination between devices. The present systems andmethods using a cloud DFS super master address these issues with priorart systems.

SUMMARY

The present disclosure relates to wireless networks and morespecifically to systems and methods for determining the location ofdistributed radar detectors and selecting available channels free ofradar signals from a plurality of radio frequency channels. Oneembodiment includes a cloud DFS super master and a radar detectorcommunicatively coupled to the cloud DFS super master. The cloud DFSsuper master is programmed to receive the results of the scan for aradar signal from the radar detector and to generate integrated clientdevice geolocation information. The cloud DFS super master is alsoprogrammed to determine a location for the radar detector based at leaston the integrated client device geolocation information, and determine aradio channel free of the radar signal based at least on the locationfor the radar detector and the results of the scan for the radar signal.

Other embodiments and various examples, scenarios and implementationsare described in more detail below. The following description and thedrawings set forth certain illustrative embodiments of thespecification. These embodiments are indicative, however, of but a fewof the various ways in which the principles of the specification may beemployed. Other advantages and novel features of the embodimentsdescribed will become apparent from the following detailed descriptionof the specification when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The aforementioned objects and advantages of the present invention, aswell as additional objects and advantages thereof, will be more fullyunderstood herein after as a result of a detailed description of apreferred embodiment when taken in conjunction with the followingdrawings in which:

FIG. 1 illustrates portions of the 5 GHz Wi-Fi spectrum includingportions that require active monitoring for radar signals.

FIG. 2 illustrates how an exemplary cloud-based intelligence engine orcloud DFS super master may interface with a conventional host accesspoint, an agility agent (an autonomous DFS master or radar detector),and client devices.

FIG. 3 illustrates how an exemplary cloud-based intelligence engine orcloud DFS super master in a peer-to-peer network may interface withclient devices and an agility agent (an autonomous DFS master or radardetector) independent of any access point.

FIG. 4 illustrates a method of performing a channel availability checkphase and in-service monitoring phase in a DFS scanning operation withan agility agent to make multiple DFS channels simultaneously availablefor use using a time-division multiplexed sequential channelavailability check followed by continuous in-service monitoring.

FIG. 5 illustrates a method of performing a channel availability checkphase and in-service monitoring phase in a DFS scanning operation withan agility agent to make multiple DFS channels simultaneously availablefor use using a continuous sequential channel availability checkfollowed by continuous in-service monitoring.

FIG. 6A illustrates a method of performing a channel availability checkphase and in-service monitoring phase in a DFS scanning operation withan agility agent to make multiple DFS channels simultaneously availablefor use.

FIG. 6B illustrates an exemplary beacon transmission duty cycle and anexemplary radar detection duty cycle.

FIG. 7 illustrates an example in which an agility agent is connected toa host device and connected to a network via the host device.

FIG. 8 illustrates an example in which an agility agent is connected toa host device and connected to a network and a cloud intelligence engineor cloud DFS super master via the host device.

FIG. 9 illustrates an example in which an agility agent is connected toa host device and connected to a network and a cloud intelligence engineor cloud DFS super master via the host device.

FIG. 10 illustrates a method of performing a channel availability checkand in-service monitoring.

FIG. 11 illustrates another method of performing a channel availabilitycheck and in-service monitoring.

FIG. 12 illustrates another method of performing a channel availabilitycheck and in-service monitoring.

FIG. 13 illustrates how multiple agility agents provide geographicallydistributed overlapping views of a radar emitter.

FIG. 14 illustrates in a control loop diagram how the cloud intelligenceengine takes the spectrum data from each agility agent, and afterstoring and filtering the data, combines it with similar data from aplurality of other agility agents and cloud data from other sources.

FIGS. 15A and 15B illustrates the logical interface between the wirelessagility agent, the cloud intelligence engine, and an access point (orsimilarly a small cell LTE-U base station).

FIG. 16A illustrates the hidden node problem where an access points orsmall cell base station is hidden from view of other access points orsmall cell base stations by topography, obstruction, distance or channelconditions.

FIG. 16B illustrates the hidden radar problem, where a radar emitter isunseen by an agility agent due to topography or obstruction.

FIG. 16C illustrates the hidden radar problem where a radar emitter isunseen by an agility agent due to distance.

FIG. 17 illustrates an exemplary embodiment of a cloud DFS super mastersystem in which the cloud DFS super master is communicatively coupled toa plurality of sensors that detect radar signals in the DFS band anddetect wireless traffic information and is communicatively coupled toone or more client devices.

FIG. 18 is an illustration of multiple radar detectors distributedthroughout a building.

FIG. 19 further illustrates the building of FIG. 18 with distributedradar detectors.

FIG. 20 is an illustration of the location information that the cloudDFS super master receives from the radar detector.

FIG. 21 is an example of the cloud DFS super master using a boundarycondition to determine the center or locus of the location information.

FIG. 22 is an illustration of more than one radar detector receivinglocation information from a client device.

FIG. 23 illustrates an exemplary method of using radar detectors and thecoupled cloud DFS super master to determine the location of the radardetectors.

DETAILED DESCRIPTION

The present disclosure relates to wireless networks and morespecifically to systems and methods for determining the location ofdistributed radar detectors and selecting available channels free ofradar signals from a plurality of radio frequency channels. Oneembodiment includes a cloud DFS super master and a radar detectorcommunicatively coupled to the cloud DFS super master. The cloud DFSsuper master is programmed to receive the results of the scan for aradar signal from the radar detector and to generate integrated clientdevice geolocation information. The cloud DFS super master is alsoprogrammed to determine a location for the radar detector based at leaston the integrated client device geolocation information, and determine aradio channel free of the radar signal based at least on the locationfor the radar detector and the results of the scan for the radar signal.

As used herein, a channel “free” of occupying signals may include achannel with occupying signals that are lower than a signal thresholdincluding signal strength, quantity, or traffic. Some embodiments employa cloud DFS super master to access additional bandwidth for wirelessnetworks, such as IEEE 802.11 a/n/ac and LTE-U networks. The additionalbandwidth is derived from channels that require avoidance of channelswith occupying signals. For example, additional bandwidth is derivedfrom special compliance channels that require radar detection, such asthe DFS channels of the U-NII-2 bands, by employing multi-channel radardetection and in-service monitoring, and active channel selectioncontrols.

FIG. 1 illustrates portions of the 5 GHz Wi-Fi spectrum 101. FIG. 1shows the frequencies 102 and channels 103 that make up portions of the5 GHz Wi-Fi spectrum 101. A U-NII band is a FCC regulatory domain for5-GHz wireless devices and is part of the radio frequency spectrum usedby IEEE 802.11 a/n/ac devices and by many wireless ISPs. It operatesover four ranges. The U-NII-1 band 105 covers the 5.15-5.25 GHz range.The U-NII-2A band 106 covers the 5.25-5.35 GHz range. The U-NII-2A band106 is subject to DFS radar detection and avoidance requirements. TheU-NII-2C band 107 covers the 5.47-5.725 GHz range. The U-NII-2C band 107is also subject to DFS radar detection and avoidance requirements. TheU-NII-3 band 109 covers the 5.725 to 5.850 GHz range. Use of the U-NII-3band 109 is restricted in some jurisdictions like the European Union andJapan.

When used in an 802.11 a/n/ac or LTE-U wireless network, the agilityagent may function as an autonomous DFS master device. In contrast toconventional DFS master devices, the agility agent is not an accesspoint or router, but rather is a standalone wireless device employinginventive scanning techniques described herein that provide DFS scancapabilities across multiple channels, enabling one or more access pointdevices and peer-to-peer client devices to exploit simultaneous multipleDFS channels. The standalone autonomous DFS master may be incorporatedinto another device such as an access point, LTE-U host, base station,cell, or small cell, media or content streamer, speaker, television,mobile phone, mobile router, software access point device, or peer topeer device but does not itself provide network access to clientdevices. In particular, in the event of a radar event, the enabledaccess point and clients or wireless device are able to moveautomatically, predictively and very quickly to another DFS channel.

FIG. 2 provides a detailed illustration of an exemplary system using acloud DFS super master. As illustrated in FIG. 2, the agility agent 200may control at least one access point or LTE-U small cell base stationto dictate channel (e.g., a communication channel associated with the 5GHz Wi-Fi spectrum 101, a communication channel associated with a 5.9GHz spectrum, a communication channel associated with a 3.5 GHzspectrum, etc.) selection primarily by (a) signaling availability of oneor more DFS channels by simultaneous transmission of one or more beaconsignals; (b) transmitting a listing of both the authorized available DFSchannels, herein referred to as a whitelist, and the prohibited DFSchannels in which a potential radar signal has been detected, hereinreferred to as a blacklist, along with control signals and a time-stampsignal, herein referred to as a dead-man switch timer via an associatednon-DFS channel; (c) transmitting the same signals as (b) over a wiredmedium such as Ethernet or serial cable; and (d) receiving control,coordination and authorized and preferred channel selection guidanceinformation from the cloud intelligence engine 235. It is to beappreciated that the cloud intelligence engine 235 can be a set of cloudintelligence devices associated with cloud-based distributedcomputational resources. For example, the cloud intelligence engine 235can be associated with multiple devices, multiple servers, multiplemachines and/or multiple clusters. As discussed in more detail below, insome embodiments the cloud intelligence engine 235 acts as a cloud DFSsuper master for connected client devices. The agility agent 200 sendsthe time-stamp signal, or dead-man switch timer, with communications toensure that the access points 218, 223 do not use the information,including the whitelist, beyond the useful lifetime of the information.For example, a whitelist will only be valid for a certain period oftime. The time-stamp signal avoids using noncompliant DFS channels byensuring that an access point will not use the whitelist beyond itsuseful lifetime. The system allows currently available access pointswithout radar detection—which cannot operate in the DFS channels—tooperate in the DFS channels by providing the radar detection required bythe FCC or other regulatory agencies. In an embodiment, the agilityagent 200 may send a status signal (e.g., a heartbeat signal) to theaccess point control agent 219 to indicate a current status and/or acurrent state of the agility agent 200. The status signal provided bythe agility agent 200 may act as a dead-man switch (e.g., in response toa local failure). Therefore, the access point control agent 219 cansafely operate on non-DFS channels. In certain implementations,authorized available DFS channels can be associated with a set ofenforcement actions that are time limited (e.g., authorized DFS channelsfor a certain geographic region can become unavailable for a few hours,etc.).

The host access point 218 and any other access point devices 223 undercontrol of the autonomous DFS master 200 typically have the controlagent portion 219, 224 installed within their communication stack. Forexample, the host access point 218 may have an access point controlagent portion 219, 224 installed within a communication stack of thehost access point 218. Furthermore, the network access point 223 mayalso have an access point control agent portion 219, 224 installedwithin a communication stack of the network access point 223. Thecontrol agent 219, 224 is an agent that acts under the direction of theagility agent 200 to receive information and commands from the agilityagent 200. The control agent 219, 224 acts on information from theagility agent 200. For example, the control agent 219, 224 listens forinformation like a whitelist or blacklist from the agility agent. If aradar signal is detected by the agility agent 200, the agility agent 200communicates that to the control agent 219, 224, and the control agent219, 224 acts to evacuate the channel immediately. The control agent canalso take commands from the agility agent 200. For example, the hostaccess point 218 and network access point 223 can offload DFS monitoringto the agility agent 200 as long as they can listen to the agility agent200 and take commands from the agility agent regarding available DFSchannels.

The host access point 218 is connected to a wide area network 233 andincludes an access point control agent 219 to facilitate communicationswith the agility agent 200. The access point control agent 219 includesa security module 220 and agent protocols 221 to facilitatecommunication with the agility agent 200, and swarm communicationprotocols 222 to facilitate communications between agility agents,access points, client devices, and other devices in the network. Theagility agent 200 connects to the cloud intelligence engine 235 via thehost access point 218 and the wide area network 233. The access pointmay set up a secure communications tunnel to communicate with the cloudintelligence engine 235 through, for example, an encrypted controlchannel associated with the host access point 218 and/or an encryptedcontrol API in the host access point 218. The agility agent 200transmits information to the cloud intelligence engine 235 such aswhitelists, blacklists, state information, location information, timesignals, scan lists (for example, showing neighboring access points),congestion (for example, number and type of re-try packets), and trafficinformation. The cloud intelligence engine 235 communicates informationto the agility agent 200 via the secure communications tunnel such asaccess point location (including neighboring access points), accesspoint/cluster current state and history, statistics (including traffic,congestion, and throughput), whitelists, blacklists, authenticationinformation, associated client information, and regional and regulatoryinformation. The agility agent 200 uses the information from the cloudintelligence engine 235 to control the access points and other networkdevices.

The agility agent 200 may communicate via wired connections orwirelessly with the other network components. In the illustratedexample, the agility agent 200 includes a primary radio 215 and asecondary radio 216. The primary radio 215 is for DFS and radardetection and is typically a 5 GHz radio. The agility agent 200 mayreceive radar signals, traffic information, and/or congestioninformation through the primary radio 215. And the agility agent 200 maytransmit information such as DFS beacons via the primary radio 215. Thesecond radio 216 is a secondary radio for sending control signals toother devices in the network and is typically a 2.4 GHz radio. Theagility agent 200 may receive information such as network traffic,congestion, and/or control signals with the secondary radio 216. And theagility agent 200 may transmit information such as control signals withthe secondary radio 216. The primary radio 215 is connected to a fastchannel switching generator 217 that includes a switch and allows theprimary radio 215 to switch rapidly between a radar detector 211 andbeacon generator 212. The channel switching generator 217 allows theradar detector 211 to switch sufficiently fast to appear to be onmultiple channels at a time. In certain implementations, the agilityagent 200 may also include coordination 253. The coordination 253 mayprovide cross-network coordination between the agility agent 200 andanother agility agent (e.g., agility agent(s) 251). For example, thecoordination 253 may provide coordination information (e.g., precisionlocation, precision position, channel allocation, a time-slice dutycycle request, traffic loading, etc.) between the agility agent 200 andanother agility agent (e.g., agility agent(s) 251) on a differentnetwork. In one example, the coordination 253 may enable an agilityagent (e.g., agility agent 200) attached to a Wi-Fi router to coordinatewith a nearby agility (e.g., agility agent(s) 251) attached to a LTE-Usmall cell base station.

A standalone multi-channel DFS master may include a beacon generator 212to generate a beacon in each of a plurality of radio channels (e.g., aplurality of 5 GHz communication channels, a plurality of 5.9 GHzcommunication channels, a plurality of 3.5 GHz communication channels,etc., for simplicity the following examples use 5 GHz), a radar detector211 to scan for a radar signal in each of the plurality of 5 GHz radiochannels, a 5 GHz radio transceiver 215 to transmit the beacon in eachof the plurality of 5 GHz radio channels and to receive the radar signalin each of the plurality of 5 GHz radio channels, and a fast channelswitching generator 217 coupled to the radar detector, the beacongenerator, and the 5 GHz radio transceiver. The fast channel switchinggenerator 217 switches the 5 GHz radio to a first channel of theplurality of 5 GHz radio channels and then causes the beacon generator212 to generate the beacon in the first channel of the plurality of 5GHz radio channels. Then the fast channel switching generator 217 causesthe radar detector 211 to scan for the radar signal in the first channelof the plurality of 5 GHz radio channels. The fast channel switchinggenerator 217 then repeats these steps for each other channel of theplurality of 5 GHz radio channels during a beacon transmission dutycycle and, in some examples, during a radar detection duty cycle. Thebeacon transmission duty cycle is the time between successive beacontransmissions on a given channel and the radar detection duty cyclewhich is the time between successive scans on a given channel. Becausethe agility agent 200 cycles between beaconing and scanning in each ofthe plurality of 5 GHz radio channels in the time window between a firstbeaconing and scanning in a given channel and a subsequent beaconing andscanning the same channel, it can provide effectively simultaneousbeaconing and scanning for multiple channels.

The agility agent 200 also may contain a Bluetooth radio 214 and an802.15.4 radio 213 for communicating with other devices in the network.The agility agent 200 may include various radio protocols 208 tofacilitate communication via the included radio devices.

The agility agent 200 may also include a location module 209 togeo-locate or otherwise determine the location of the agility agent 200.Information provided by the location module 209 may be employed tolocation-tag and/or time-stamp spectral information collected and/orgenerated by the agility agent 200. As shown in FIG. 2, the agilityagent 200 may include a scan and signaling module 210. The agility agent200 includes embedded memory 202, including for example flash storage201, and an embedded processor 203. The cloud agent 204 in the agilityagent 200 facilitates aggregation of information from the cloud agent204 through the cloud and includes swarm communication protocols 205 tofacilitate communications between agility agents, access points, clientdevices, and other devices in the network. The cloud agent 204 alsoincludes a security module 206 to protect and secure the agility agent's200 cloud communications as well as agent protocols 207 to facilitatecommunication with the access point control agents 219, 224.

As shown in FIG. 2, the agility agent 200 may control other accesspoints, for example networked access point 223, in addition to the hostaccess point 218. The agility agent 200 may communicate with the otheraccess points 223 via a wired or wireless connection 236, 237. In oneexample, the agility agent 200 may communicate with the other accesspoints 223 via a local area network. The other access points 223 includean access point control agent 224 to facilitate communication with theagility agent 200 and other access points. The access point controlagent 224 includes a security module 225, agent protocols 226 and swarmcommunication protocols 227 to facilitate communications with otheragents (including other access points and client devices) on thenetwork.

The cloud intelligence engine 235 includes a database 248 and memory 249for storing information from the agility agent 200, other agility agents(not shown) connected to the intelligence engine 235, and external datasources. The database 248 and memory 249 allow the cloud intelligenceengine 235 to store information over months and years received fromagility agents and external data sources. The data source(s) 252 may beassociated with a set of databases. Furthermore, the data source(s) 252may include regulatory information (e.g., non-spectral information) suchas, but not limited to, geographical information system (GIS)information, other geographical information, FCC information regardingthe location of radar transmitters, FCC blacklist information, NationalOceanic and Atmospheric Administration (NOAA) databases, Department ofDefense (DoD) information regarding radar transmitters, DoD requests toavoid transmission in DFS channels for a given location, and/or otherregulatory information.

The cloud intelligence engine 235 also includes processors 250 toperform the cloud intelligence operations described herein. The roamingand guest agents manager 238 in the cloud intelligence engine 235provides optimized connection information for devices connected toagility agents that are roaming from one access point to other or fromone access point to another network. The roaming and guest agentsmanager 238 also manages guest connections to networks for agilityagents connected to the cloud intelligence engine 235. The external datafusion engine 239 provides for integration and fusion of informationfrom agility agents with information from external data sources forexample GIS information, other geographical information, FCC informationregarding the location of radar transmitters, FCC blacklist information,NOAA databases, DoD information regarding radar transmitters, and DoDrequests to avoid transmission in DFS channels for a given location. Thecloud intelligence engine 235 further includes an authenticationinterface 240 for authentication of received communications and forauthenticating devices and users. The radar detection compute engine 241aggregates radar information from agility agents and external datasources and computes the location of radar transmitters from those datato, among other things, facilitate identification of false positiveradar detections or hidden nodes and hidden radar. The radar detectioncompute engine 241 may also guide or steer multiple agility agents todynamically adapt detection parameters and/or methods to further improvedetection sensitivity. The location compute and agents manager 242determines the location the agility agent 200 and other connecteddevices through Wi-Fi lookup in a Wi-Fi location database, queryingpassing devices, triangulation based on received signal strengthindication (RSSI), triangulation based on packet time-of-flight, scanlists from agility agents, multiangulation, trilateration,multilateration, and/or geometric inference.

The spectrum analysis and data fusion engine 243 and the networkoptimization self-organization engine 244 facilitate dynamic spectrumoptimization with information from the agility agents and external datasources. Each of the agility agents connected to the cloud intelligenceengine 235 have scanned and analyzed the local spectrum and communicatedthat information to the cloud intelligence engine 235. The cloudintelligence engine 235 also knows the location of each agility agentand the access points proximate to the agility agents that do not have acontrolling agent as well as the channel on which each of those devicesis operating. With this information, the spectrum analysis and datafusion engine 243 and the network optimization self-organization engine244 can optimize the local spectrum by telling agility agents to avoidchannels subject to interference. The swarm communications manager 245manages communications between agility agents, access points, clientdevices, and other devices in the network. The cloud intelligence engineincludes a security manager 246. The control agents manager 247 managesall connected control agents. In an implementation, the cloudintelligence engine 235 may enable the host access point 218 tocoordinate network configurations with same networks (e.g., Wi-Fi toWi-Fi) and/or across different networks (e.g., Wi-Fi to LTE-U).Furthermore, the cloud intelligence engine 235 may enable agility agents(e.g., agility agent 200 and agility agent(s) 251) connected todifferent host access devices to communicate within a same network(e.g., Wi-Fi to Wi-Fi) and/or across a different network (e.g., Wi-Fi toLTE-U).

Independent of a host access point 218, the agility agent 200, in therole of an autonomous DFS master device, may also provide the channelindication and channel selection control to one or more peer-to-peerclient devices 231, 232 within the coverage area by (a) signalingavailability of one or more DFS channels by simultaneous transmission ofone or more beacon signals; (b) transmitting a listing of both theauthorized available DFS channels, herein referred to as a whitelist andthe prohibited DFS channels in which a potential radar signal has beendetected, herein referred to as a blacklist along with control signalsand a time-stamp signal, herein referred to as a dead-man switch timervia an associated non-DFS channel; and (c) receiving control,coordination and authorized and preferred channel selection guidanceinformation from the cloud intelligence engine 235. The agility agent200 sends the time-stamp signal, or dead-man switch timer, withcommunications to ensure that the devices do not use the information,including the whitelist, beyond the useful lifetime of the information.For example, a whitelist will only be valid for a certain period oftime. The time-stamp signal avoids using noncompliant DFS channels byensuring that a device will not use the whitelist beyond its usefullifetime. Alternatively, the cloud intelligence engine 235 acting as acloud DFS super master may provide available channels to the clientdevices.

Such peer-to-peer devices may have a user control interface 228. Theuser control interface 228 includes a user interface 229 to allow theclient devices 231, 232 to interact with the agility agent 200 via thecloud intelligence engine 235. For example, the user interface 229allows the user to modify network settings via the agility agent 200including granting and revoking network access. The user controlinterface 228 also includes a security element 230 to ensure thatcommunications between the client devices 231, 232 and the agility agent200 are secure. The client devices 231, 232 are connected to a wide areanetwork 234 via a cellular network for example. In certainimplementations, peer-to-peer wireless networks are used for directcommunication between devices without an access point. For example,video cameras may connect directly to a computer to download video orimages files using a peer-to-peer network. Also, device connections toexternal monitors and device connections to drones currently usepeer-to-peer networks. Therefore, in a peer-to-peer network without anaccess point, DFS channels cannot be employed since there is no accesspoint to control DFS channel selection and/or to tell devices which DFSchannels to use.

FIG. 3 illustrates how the agility agent 200 in a peer-to-peer network300 (a local area network for example) would interface to client devices231, 232, 331 and the cloud intelligence engine 235 independent of anyaccess point. As shown in FIG. 3, the cloud intelligence engine 235 maybe connected to a plurality of network-connected agility agents 200,310. The agility agent 200 in the peer-to-peer network 300 may connectto the cloud intelligence engine 235 through one of thenetwork-connected client devices 231, 331 by, for example, piggy-backinga message to the cloud intelligence engine 235 on a message send to theclient devices 231, 331 or otherwise coopting the client devices' 231,331 connection to the wide area network 234. In the peer-to-peer network300, the agility agent 200 sends over-the-air control signals 320 to theclient devices 231, 232, 331 including indications of channels free ofoccupying signals such as DFS channels free of radar signals.Alternatively, the agility agent communicates with just one clientdevice 331 which then acts as the group owner to initiate and controlthe peer-to-peer communications with other client devices 231, 232. Theclient devices 231, 232, 331 have peer-to-peer links 321 through whichthey communicate with each other.

The agility agent may operate in multiple modes executing a number ofDFS scan methods employing different algorithms. Two of these methodsare illustrated in FIG. 4 and FIG. 5.

FIG. 4 illustrates a first DFS scan method 400 for a multi-channel DFSmaster. This method uses a time division sequential CAC 401 followed bycontinuous ISM 402. The method begins at step 403 with the multi-channelDFS master at startup or after a reset. At step 404 the embedded radiois set to receive (Rx) and is tuned to the first DFS channel (C=1). Inone example, the first channel is channel 52. Next, because this is thefirst scan after startup or reset and the DFS master does not haveinformation about channels free of radar, the DFS master performs acontinuous CAC 405 scan for a period of 60 seconds (compliant with theFCC Part 15 Subpart E and ETSI 301 893 requirements). At step 406 theDFS master determines if a radar pattern is present in the currentchannel. If radar pattern is detected 407, then the DFS master marksthis channel in the blacklist. The DFS master may also send additionalinformation about the detected radar including the signal strength,radar pattern, type of radar, and a time stamp for the detection.

At the first scan after startup or reset, if a radar pattern is detectedin the first channel scanned, the DFS master may repeat the above stepsuntil a channel free of radar signals is found. Alternatively, after astartup or reset, the DFS master may be provided a whitelist indicatingone or more channels that have been determined to be free of radarsignals. For example, the DFS master may receive a message that channel52 is free of radar signals from the cloud intelligence engine 235 alongwith information fused from other sources.

If at step 406 the DFS master does not detect a radar pattern 410, theDFS master marks this channel in the whitelist and switches the embeddedradio to transmit (Tx) (not shown in FIG. 4) at this channel. The DFSmaster may include additional information in the whitelist including atime stamp. The DFS master then transmits (not shown in FIG. 4) a DFSmaster beacon signal for minimum required period of n (which is theperiod of the beacon transmission defined by IEEE 802.11 requirements,usually very short on the order of a few microseconds). A common SSIDmay be used for all beacons of our system.

For the next channel scan after the DFS master finds a channel free ofradar, the DFS master sets the radio to receive and tunes the radio tothe next DFS channel 404 (for example channel 60). The DFS master thenperforms a non-continuous CAC radar detection scan 405 for period of X,which is the maximum period between beacons allowable for a clientdevice to remain associated with a network (P_(M)) less a period of nrequired for a quick radar scan and the transmission of the beaconitself (X=P_(M)−n) 408. At 411, the DFS master saves the state ofcurrent non-continuous channel state (S_(C)) from the non-continuous CACscan so that the DFS master can later resume the current non-continuouschannel scan at the point where the DFS master left off. Then, at step412, the DFS master switches the radio to transmit and tunes to thefirst DFS channel (in this example it was CH 52), performs quick receiveradar scan 413 (for a period of D called the dwell time) to detect radar414. If a radar pattern is detected, the DFS master marks the channel tothe blacklist 418. When marking the channel to the blacklist, the DFSmaster may also include additional information about the detected radarpattern including signal strength, type of radar, and a time stamp forthe detection. The type of radar detected includes information such asburst duration, number of bursts, pulses per burst, burst period, scanpattern, pulse repetition rate and interval, pulse width, chirp width,beam width, scan rate, pulse rise and fall times, frequency modulation,frequency hopping rate, hopping sequence length, and pulses per hop.

If no radar pattern is detected, the DFS master transmits again 415 theDFS master beacon for the first channel (channel 52 in the example).Next, the DFS master determines if the current channel (C_(B)) is thelast channel in the whitelist (W_(L)) 416. In the current example, thecurrent channel, channel 52, is the only channel in the whitelist atthis point. Then, the DFS master restores 417 the channel to the savedstate from step 411 and switches the radio back to receive mode andtunes the radio back to the current non-continuous CAC DFS channel(channel 60 in the example) 404. The DFS master then resumes thenon-continuous CAC radar scan 405 for period of X, again accommodatingthe period of n required for the quick scan and transmission of thebeacon. This is repeated until 60 seconds of non-continuous CAC scanningis accumulated 409—in which case the channel is marked in the whitelist410—or until a radar pattern is detected—in which case this channel ismarked in the blacklist 407.

Next, the DFS master repeats the procedure in the preceding paragraphfor the next DFS channel (for example channel 100). The DFS masterperiodically switches 412 to previous whitelisted DFS channels to do aquick scan 413 (for a period of D called the dwell time), and if noradar pattern detected, transmits a beacon 415 for period of n in eachof the previously CAC scanned and whitelisted DFS channels. Then the DFSmaster returns 404 to resume the non-continuous CAC scan 405 of thecurrent CAC channel (in this case CH 100). The period X available fornon-continuous CAC scanning before switching to transmit andsequentially beaconing the previously whitelisted CAC scanned channelsis reduced by n for each of the previously whitelisted CAC scannedchannels, roughly X=P_(M)−n*(W_(L)) where W_(L) is the number ofpreviously whitelisted CAC scanned channels. This is repeated until 60seconds of non-continuous CAC scanning is accumulated for the currentchannel 409. If no radar pattern is detected the channel is marked inthe whitelist 410. If a radar pattern is detected, the channel is markedin the blacklist 407 and the radio can immediately switch to the nextDFS channel to be CAC scanned.

The steps in the preceding paragraph are repeated for each new DFSchannel until all desired channels in the DFS band have been CACscanned. In FIG. 4, step 419 checks to see if the current channel C isthe last channel to be CAC scanned R. If the last channel to be CACscanned R has been reached, the DFS master signals 420 that the CACphase 401 is complete and begins the ISM phase 402. The whitelist andblacklist information may be communicated to the cloud intelligenceengine where it is integrated over time and fused with similarinformation from other agility agents.

During the ISM phase, the DFS master does not scan the channels in theblacklist 421. The DFS master switches 422 to the first channel in thewhitelist and transmits 423 a DFS beacon on that channel. Then the DFSmaster scans 424 the first channel in the whitelist for a period ofD_(ISM) (the ISM dwell time) 425, which may be roughly P_(M) (themaximum period between beacons allowable for a client device to remainassociated with a network) minus n times the number of whitelistedchannels, divided by the number of whitelisted channels(D_(ISM)=(P_(M)−n*W_(L))/n). Then the DFS master transmits 423 a beaconand scans 424 each of the channels in the whitelist for the dwell timeand then repeats starting at the first channel in the whitelist 422 in around robin fashion for each respective channel. If a radar pattern isdetected 426, the DFS master beacon for the respective channel isstopped 427, and the channel is marked in the blacklist 428 and removedfrom the whitelist (and no longer ISM scanned). The DFS master sendsalert messages 429, along with the new whitelist and blacklist to thecloud intelligence engine. Alert messages may also be sent to otheraccess points and/or client devices in the network.

FIG. 5 illustrates a second DFS scan method 500 for a multi-channel DFSmaster. This method uses a continuous sequential CAC 501 followed bycontinuous ISM 502. The method begins at step 503 with the multi-channelDFS master at startup or after a reset. At step 504 the embedded radiois set to receive (Rx) and is tuned to the first DFS channel (C=1). Inthis example, the first channel is channel 52. The DFS master performs acontinuous CAC scan 505 for a period of 60 seconds 507 (compliant withthe FCC Part 15 Subpart E and ETSI 301 893 requirements). If radarpattern is detected at step 506 then the DFS master marks this channelin the blacklist 508.

If the DFS master does not detect radar patterns, it marks this channelin the whitelist 509. The DFS master determines if the current channel Cis the last channel to be CAC scanned R at step 510. If not, then theDFS master tunes the receiver to the next DFS channel (for examplechannel 60) 504. Then the DFS master performs a continuous scan 505 forfull period of 60 seconds 507. If a radar pattern is detected, the DFSmaster marks the channel in the blacklist 508 and the radio canimmediately switch to the next DFS channel 504 and repeat the stepsafter step 504.

If no radar pattern is detected 509, the DFS master marks the channel inthe whitelist 509 and then tunes the receiver next DFS channel 504 andrepeats the subsequent steps until all DFS channels for which a CAC scanis desired. Unlike the method depicted in FIG. 4, no beacon istransmitted between CAC scans of sequential DFS channels during the CACscan phase.

The ISM phase 502 in FIG. 5 is identical to that in FIG. 4 describedabove.

FIG. 6A illustrates how multiple channels in the DFS channels of the 5GHz band are made simultaneously available by use of an agility agent.FIG. 6A illustrates the process of FIG. 5 wherein the autonomous DFSMaster performs the DFS scanning CAC phase 600 across multiple channelsand upon completion of CAC phase, the autonomous DFS Master performs theISM phase 601. During the ISM phase the DFS master transmits multiplebeacons to indicate the availability of multiple DFS channels to nearbyhost and non-host (ordinary) access points and client devices.

FIG. 6A shows the frequencies 602 and channels 603 that make up portionsof the DFS 5 GHz Wi-Fi spectrum. U-NII-2A 606 covers the 5.25-5.35 GHzrange. U-NII-2C 607 covers the 5.47-5.725 GHz range. The first channelto undergo CAC scanning is shown at element 607. The subsequent CACscans of other channels are shown at elements 608. And the final CACscan before the ISM phase 601 is shown at element 609.

In the ISM phase 601, the DFS master switches to the first channel inthe whitelist. In the example in FIG. 6A, each channel 603 for which aCAC scan was performed was free of radar signals during the CAC scan andwas added to the whitelist. Then the DFS master transmits 610 a DFSbeacon on that channel. Then the DFS master scans 620 the first channelin the whitelist for the dwell time. Then the DFS master transmits 611 abeacon and scans 621 each of the other channels in the whitelist for thedwell time and then repeats starting 610 at the first channel in thewhitelist in a round robin fashion for each respective channel. If aradar pattern is detected, the DFS master beacon for the respectivechannel is stopped, and the channel is marked in the blacklist andremoved from the whitelist (and no longer ISM scanned).

FIG. 6A also shows an exemplary waveform 630 of the multiple beacontransmissions from the DFS master to indicate the availability of themultiple DFS channels to nearby host and non-host (ordinary) accesspoints and client devices.

FIG. 6B illustrates a beacon transmission duty cycle 650 and a radardetection duty cycle 651. In this example, channel A is the firstchannel in a channel whitelist. In FIG. 6B, a beacon transmission inchannel A 660 is followed by a quick scan of channel A 670. Next abeacon transmission in the second channel, channel B, 661 is followed bya quick scan of channel B 671. This sequence is repeated for channels C662, 672; D 663, 673; E 664, 674; F 665, 675; G 666, 676, and H 667,677. After the quick scan of channel H 677, the DFS master switches backto channel A and performs a second beacon transmission in channel A 660followed by a second quick scan of channel A 670. The time betweenstarting the first beacon transmission in channel A and starting thesecond beacon transmission in channel A is a beacon transmission dutycycle. The time between starting the first quick scan in channel A andstarting the second quick scan in channel A is a radar detection dutycycle. In order to maintain connection with devices on a network, thebeacon transmission duty cycle should be less than or equal to themaximum period between the beacons allowable for a client device toremain associated with the network.

A standalone multi-channel DFS master may include a beacon generator 212to generate a beacon in each of a plurality of 5 GHz radio channels, aradar detector 211 to scan for a radar signal in each of the pluralityof 5 GHz radio channels, a 5 GHz radio transceiver 215 to transmit thebeacon in each of the plurality of 5 GHz radio channels and to receivethe radar signal in each of the plurality of 5 GHz radio channels, and afast channel switching generator 217 and embedded processor 203 coupledto the radar detector, the beacon generator, and the 5 GHz radiotransceiver. The fast channel switching generator 217 and embeddedprocessor 203 switch the 5 GHz radio transceiver 215 to a first channelof the plurality of 5 GHz radio channels and cause the beacon generator212 to generate the beacon in the first channel of the plurality of 5GHz radio channels. The fast channel switching generator 217 andembedded processor 203 also cause the radar detector 211 to scan for theradar signal in the first channel of the plurality of 5 GHz radiochannels. The fast channel switching generator 217 and embeddedprocessor 203 then repeat these steps for each of the other channels ofthe plurality of 5 GHz radio channels. The fast channel switchinggenerator 217 and embedded processor 203 perform all of the steps forall of the plurality of 5 GHz radio channels during a beacontransmission duty cycle which is a time between successive beacontransmissions on a specific channel and, in some examples, a radardetection duty cycle which is a time between successive scans on thespecific channel.

The example in FIG. 7 illustrates systems and methods for selectingavailable channels free of occupying signals from a plurality of radiofrequency channels. The system includes an agility agent 700 functioningas an autonomous frequency selection master that has both an embeddedradio receiver 702 to detect the occupying signals in each of theplurality of radio frequency channels and an embedded radio transmitter703 to transmit an indication of the available channels and anindication of unavailable channels not free of the occupying signals.The agility agent 700 is programmed to connect to a host device 701 andcontrol a selection of an operating channel selection of the host deviceby transmitting the indication of the available channels and theindication of the unavailable channels to the host device. The hostdevice 701 communicates wirelessly with client devices 720 and acts as agateway for client devices to a network 710 such as the Internet, otherwide area network, or local area network. The host device 701, under thecontrol of the agility agent 700, tells the client devices 720 whichchannel or channels to use for wireless communication. Additionally, theagility agent 700 may be programmed to transmit the indication of theavailable channels and the indication of the unavailable channelsdirectly to client devices 720.

The agility agent 700 may operate in the 5 GHz band and the plurality ofradio frequency channels may be in the 5 GHz band and the occupyingsignals are radar signals. The host device 701 may be a Wi-Fi accesspoint or an LTE-U host device.

Further, the agility agent 700 may be programmed to transmit theindication of the available channels by transmitting a channel whitelistof the available channels and to transmit the indication of theunavailable channels by transmitting a channel blacklist of theunavailable channels. In addition to saving the channel in the channelblacklist, the agility agent 700 may also be programmed to determine andsave in the channel blacklist information about the detected occupyingsignals including signal strength, traffic, and type of the occupyingsignals.

As shown in FIG. 8, the agility agent 700 may be connected to acloud-based intelligence engine 855. The agility agent 700 may connectto the cloud intelligence engine 855 directly or through the host device701 and network 710. The cloud intelligence engine 855 integrates timedistributed information from the agility agent 700 and combinesinformation from a plurality of other agility agents 850 distributed inspace and connected to the cloud intelligence engine 855. The agilityagent 700 is programmed to receive control and coordination signals andauthorized and preferred channel selection guidance information from thecloud intelligence engine 755.

The example shown in FIG. 9 shows a system and method for selectingavailable channels free of occupying signals from a plurality of radiofrequency channels in which an agility agent 700 functioning as anautonomous frequency selection master includes an embedded radioreceiver 702 to detect the occupying signals in each of the plurality ofradio frequency channels and an embedded radio transmitter 703 toindicate the available channels and unavailable channels not free of theoccupying signals. The agility agent 700 contains a channel whitelist910 of one or more channels scanned and determined not to contain anoccupying signal. The agility agent 700 may receive the whitelist 910from another device including a cloud intelligence engine 855. Or theagility agent 700 may have previously derived the whitelist 910 througha continuous CAC for one or more channels. In this example, the agilityagent 700 is programmed to cause the embedded radio receiver 702 to scaneach of the plurality of radio frequency channels non-continuouslyinterspersed with periodic switching to the channels in the channelwhitelist 910 to perform a quick occupying signal scan in each channelin the channel whitelist 910. The agility agent 700 is furtherprogrammed to cause the embedded radio transmitter 703 to transmit afirst beacon transmission in each channel in the channel whitelist 910during the quick occupying signal scan and to track in the channelwhitelist 910 the channels scanned and determined not to contain theoccupying signal during the non-continuous scan and the quick occupyingsignal scan. The agility agent 700 is also programmed to track in achannel blacklist 915 the channels scanned and determined to contain theoccupying signal during the non-continuous scan and the quick occupyingsignal scan and then to perform in-service monitoring for the occupyingsignal, including transmitting a second beacon for each of the channelsin the channel whitelist 910, continuously and sequentially.

FIG. 10 illustrates an exemplary method 1000 for selecting an operatingchannel from a plurality of radio frequency channels in an agility agentfunctioning as an autonomous frequency selection master. The methodincludes receiving a channel whitelist of one or more channels scannedand determined not to contain an occupying signal 1010. Next, theagility agent performs a channel availability check 1005 for theplurality of radio frequency channels in a time-division manner. Thetime-division channel availability check includes scanning 1010 with anembedded radio receiver in the agility agent each of the plurality ofradio frequency channels non-continuously interspersed with periodicswitching to the channels in the channel whitelist to perform a quickoccupying signal scan and transmitting 1020 a first beacon with anembedded radio transmitter in the agility agent in each channel in thechannel whitelist during the quick occupying signal scan. The agilityagent also tracks 1030 in the channel whitelist the channels scanned instep 1010 and determined not to contain the occupying signal and tracks1040 in a channel blacklist the channels scanned in step 1010 anddetermined to contain the occupying signal. Finally, the agility agentperforms in-service monitoring for the occupying signal and a secondbeaconing transmission for each of the channels in the channel whitelistcontinuously and sequentially 1050.

FIG. 11 illustrates another exemplary method 1100 for selecting anoperating channel from a plurality of radio frequency channels in anagility agent functioning as an autonomous frequency selection master.The method 1100 includes performing a channel availability check foreach of the plurality of radio frequency channels by scanning 1101 withan embedded radio receiver in the agility agent each of the plurality ofradio frequency channels continuously for a scan period. The agilityagent then tracks 1110 in a channel whitelist the channels scanned anddetermined not to contain an occupying signal and tracks 1120 in achannel blacklist the channels scanned and determined to contain theoccupying signal. Then the agility agent performs in-service monitoringfor the occupying signal and transmits a beacon with an embedded radiotransmitter in the agility agent for each of the channels in the channelwhitelist continuously and sequentially 1130.

FIG. 12 illustrates a further exemplary method 1200 for selecting anoperating channel from a plurality of radio frequency channels in anagility agent functioning as an autonomous frequency selection master.The method 1200 includes performing a channel availability check 1210for each of the plurality of radio frequency channels and performingin-service monitoring and beaconing 1250 for each of the plurality ofradio frequency channels. The channel availability check 1210 includestuning an embedded radio receiver in the autonomous frequency selectionmaster device to one of the plurality of radio frequency channels andinitiating a continuous channel availability scan in the one of theplurality of radio frequency channels with the embedded radio receiver1211. Next, the channel availability check 1210 includes determining ifan occupying signal is present in the one of the plurality of radiofrequency channels during the continuous channel availability scan 1212.If the occupying signal is present in the one of the plurality of radiofrequency channels during the continuous channel availability scan, thechannel availability check 1210 includes adding the one of the pluralityof radio frequency channels to a channel blacklist and ending thecontinuous channel availability scan 1213. If the occupying signal isnot present in the one of the plurality of radio frequency channelsduring the continuous channel availability scan during a first scanperiod, the channel availability check 1210 includes adding the one ofthe plurality of radio frequency channels to a channel whitelist andending the continuous channel availability scan 1214. Next, the channelavailability check 1210 includes repeating steps 1211 and 1212 andeither 1213 or 1214 for each of the plurality of radio frequencychannels.

The in-service monitoring and beaconing 1250 for each of the pluralityof radio frequency channels includes determining if the one of theplurality of radio frequency channels is in the channel whitelist and ifso, tuning the embedded radio receiver in the autonomous frequencyselection master device to the one of the plurality of radio frequencychannels and transmitting a beacon in the one of the plurality of radiofrequency channels with an embedded radio transmitter in the autonomousfrequency selection master device 1251. Next, the in-service monitoringand beaconing 1250 includes initiating a discrete channel availabilityscan (a quick scan as described previously) in the one of the pluralityof radio frequency channels with the embedded radio receiver 1252. Next,the in-service monitoring and beaconing 1250 includes determining if theoccupying signal is present in the one of the plurality of radiofrequency channels during the discrete channel availability scan 1253.If the occupying signal is present, the in-service monitoring andbeaconing 1250 includes stopping transmission of the beacon, removingthe one of the plurality of radio frequency channels from the channelwhitelist, adding the one of the plurality of radio frequency channelsto the channel blacklist, and ending the discrete channel availabilityscan 1254. If the occupying signal is not present in the one of theplurality of radio frequency channels during the discrete channelavailability scan for a second scan period, the in-service monitoringand beaconing 1250 includes ending the discrete channel availabilityscan 1255. Thereafter, the in-service monitoring and beaconing 1250includes repeating steps 1251, 1252, and 1253 as well as either 1254 or1255 for each of the plurality of radio frequency channels.

As discussed herein, the disclosed systems are fundamentally differentfrom the current state of art in that: (a) the disclosed wirelessagility agents enable multiple simultaneous dynamic frequency channels,which is significantly more bandwidth than provided by conventionalstandalone DFS master access points or small cell base stations; (b) theadditional DFS channels may be shared with nearby (suitably equippedwith a control agent) access points or small cells, enabling the networkas a whole to benefit from the additional bandwidth; and (c) theselection of operating channels by the access points and/or small cellbase stations can be coordinated by a centralized network organizationelement (the cloud intelligence engine) to avoid overlapping channelsthus avoiding interference and relieving congestion.

The capability and functions in (a) to (c) are enabled by thecentralized cloud intelligence engine which collects and combines theDFS radar and other spectrum information from each agility agent andgeo-tags, stores, filters, and integrates the data over time, andcombines it together by data fusion technique with information from aplurality of other agility agents distributed in space, and performsfiltering and other post-processing on the collection with proprietaryalgorithms, and merges with other data from vetted sources (such as GIS,Federal Aviation Administration (FAA), FCC, and DoD databases, etc.).

Specifically, the cloud intelligence engine performs the following:continuously collects the spectrum, location and networkcongestion/traffic information from all wireless agility agents, thenumber and density of which grows rapidly as more access points andsmall cell base stations are deployed; continuously applyingsophisticated filtering, spatial and time correlation and integrationoperations, and novel array-combining techniques, and patternrecognition, etc. across the data sets; applying inventive networkanalysis and optimization techniques to compute network organizationdecisions to collectively optimize dynamic channel selection of accesspoints and small cell base stations across networks; and directing theadaptive control of dynamic channel selection and radio configuration of802.11 a/n/ac access points and/or LTE-U small cell base stations viasaid wireless agility agents.

Agility agents, due to their attachment to Wi-Fi access points and LTE-Usmall cell base stations, are by nature deployed over wide geographicalareas in varying densities and often with overlapping coverage. Thus thespectrum information collected by agility agents, in particular thesignatures of DFS radar and congestion conditions of local networks,similarly represent multi-point overlapping measurements of the radiospectrum over wide areas, or viewed a different way, the informationrepresents spectrum measurements by random irregular arrays of sensorsmeasuring radar and sources of interference and/or congestion fromdifferent angles (see FIG. 13).

FIG. 13 illustrates how multiple agility agents 1311, 1312, 1313, 1314(for example, each attached to an 802.11 a/n/ac Wi-Fi network) providegeographically distributed overlapping views (sets of sensor data) of aradar emitter 1350. The figure also shows how by reporting to thecentralized cloud intelligence engine 235, the collective multiple viewdata when pieced together by the cloud intelligence engine 235 takes onthe attributes of both spatial diversity (different range andfading/reflective channel conditions 1321, 1322, 1323, 1324) and angulardiversity (for example, look angles 1331, 1332, 1333, 1334) all of whichcan thus be leveraged to generate a pseudo synthetic aperture view ofthe target radar 1350 or any other emitter source with considerably moreeffective gain and sensitivity than was represented by any single viewfrom a single access point or small cell base station. Differentpositions 1321, 1322, 1323, 1324 and look angles 1331, 1332, 1333, 1334results in different timing offset of received radar pulse train anddifferent distortion of received signal due to different fading andreflective channel conditions. A subset of the agility agents 1311,1312, 1313, 1314 may form a pseudo-synthetic antenna array that providesimproved sensitivity to radar signals due to effective higher gain androbustness in radar detection due to redundancy. The data from theagility agents 1311, 1312, 1313, 1314 are transmitted to the cloudintelligence engine 235 which performs data correlation and integrationto determine the location of the target radar 1350.

The cloud intelligence engine having considerable processingcapabilities and infinitely scalable memory/storage, is able to storethe time-stamped spectrum information from each agility agent over verylong periods of time, thus enabling the cloud intelligence engine toalso integrate and correlate the signatures of DFS radar and congestionconditions of the local network over time as well as over geographicspace. Given a sufficient number of agility agents continuouslyacquiring spectral information over time, the cloud intelligence enginecan construct an increasingly accurate and reliable spatial map ofspectrum information in the 5 GHz band, including the presence orabsence of radar signals. The spectral information may belocation-tagged and/or time-stamped. The device may be, for example, anaccess point device, a DFS slave device, a peer-to-peer group ownerdevice, a mobile hotspot device, a radio access node device or adedicated sensor node device. With this information, client devices candirectly query the cloud intelligence engine to find out what DFSchannels are available and free of radar at the location of the clientdevice. With this system, the client device no longer needs to wait fora beacon that would have otherwise been provided by an access point oragility agent as the client device can communicate with the cloudintelligence engine via a network connection to determine the availablechannels. In this situation, the cloud intelligence engine becomes acloud DFS super master as it can provide DFS channel selectioninformation for a plurality of client devices distributed over a widerange of geographies.

Further, the cloud intelligence engine is also able to access andcombine data from other sources (data fusion), such as topographic andmap information from GIS (Geographical Information System) servers, FCCdatabases, NOAA databases, etc. enabling the cloud intelligence engineto further compare, correlate, overlay and otherwise polish the baselinespectrum data from agility agents and augment the networkself-organization algorithm to further improve the overall accuracy androbustness.

The cloud intelligence engine having thus formed a detailed picture ofthe dynamic spectrum conditions of 802.11 a/n/ac and LTE-U networks isable to use this data to compute optimal network configurations, inparticular the selection of operating channels (in both DFS and non-DFSbands) and radio parameters, of individual access points and/or smallcell base stations to avoid overlap with other nearby access points orbase stations, interferers, and noisy or congested channels. The overallsystem embodied by this can thus be viewed as a large wide-area closedcontrol system, as illustrated in FIG. 14.

In one example, a system includes a cloud DFS super master and aplurality of radar detectors communicatively coupled to the cloud DFSsuper master. The radar detectors are programmed to scan for a radarsignal in each of a plurality of 5 GHz radio channels, to transmit theresults of the scan for the radar signal to the cloud DFS super master,and to transmit geo-location information for each of the plurality ofradar detectors to the cloud DFS super master. The cloud DFS supermaster is programmed to receive the results of the scan for the radarsignal from each of the plurality of radar detectors and thegeo-location information for the plurality of radar detectors anddetermine if a first radar detector of the plurality of radar detectorsdetected the radar signal in a first channel of the plurality of 5 GHzradio channels. If the cloud DFS super maser determines that the radarsignal is present in the first channel, the cloud DFS super master isprogrammed to determine one or more radar detector (e.g., second radardetectors) of the plurality of radar detectors to evaluate the firstradar detector's detection of the radar signal in the first channelbased on the geo-location information for the first radar detector andthe geo-location for the second radar detector. In one example, thecloud DFS super master is programmed to cause the one or more secondradar detectors to switch to the first channel and scan for radar in thefirst channel. And in another example, the cloud DFS super master isprogrammed to cause the one or more second radar detectors increase adwell time in the first channel. In these examples, the cloud DFS supermaster can coordinate the radar detectors when any one detector seesradar. The cloud DFS super master and network of radar detectors actslike a large synthetic aperture array, and the cloud DFS super mastercan control the radar detectors to take action. Some of the actionsinclude moving one or more radar detector to the channel in which radarwas detected and looking for radar or causing one or more radardetectors to dwell longer in the channel in which radar was detected.The more sensors looking at the radar signal, the better the radarsignal can be characterized. Further, through geo-location the cloud DFSsupertaster may determine that there are other detectors in a betterposition to measure or characterize the radar and may use data from oneor more detectors (e.g., fusing data from multiple detectors). Thiscould be driven by historical data or by knowing the type/model ofdetectors. Indeed, as sensors are upgraded their sensitivity may bebetter than previous generation of products. The cloud DFS supertastermay track what detectors (and their capabilities) are deployed in agiven area and optimally select which ones will provide the secondaryverifying radar scans.

FIG. 14 illustrates in a control loop diagram how the cloud intelligenceengine takes the spectrum data (radar lists and patterns, whitelists,blacklists, RSSI, noise floor, nearest neighbors, congestion & trafficsignatures, etc.) from a network of agility agents (e.g., each of theglobal network of agility agents 1410), and after storing (in storage1425) and filtering the data, combines them with similar data from anagility agent 1411, cloud data 1420 from other sources (such as the GIS,FCC, FAA, DoD, NOAA, etc.), and user input 1435. Then applying the datato the network self-organization compute process 1426, the control loopperforms optimum dynamic channel selection 1455 for each of the 802.11a/n/ac access points or LTE-U small cell base stations in the network(s)and under control of an example system. In this way, the cloudintelligence engine tells the agility agent 1411 to change to theselected channel 1455 for the access point (using access point control1412) from the current channel 1456 (the channel previously used by theaccess point). In contrast, conventional access points and small cellbase stations behave as open control loops with limited single-sourcesensor input and without the benefit of the cloud intelligence engine toclose the control loop.

Information (including spectral and location information) from theagility agent 1411 is used with information from a location database1451 to resolve the location 1450 of the agility agent 1411 and the802.11 a/n/ac access points or LTE-U small cell base stations in thenetwork(s) and under control of the agility agent 1411. The lookup 1441accesses stored data from the agility agents 1410. This information canbe combined with the information from the resolve location step 1450 forgeometric extrapolation 1442 of spectral conditions applicable foragility agent 1411 and the 802.11 a/n/ac access points or LTE-U smallcell base stations in the network(s) and under control of the agilityagent 1411.

As illustrated in FIG. 14, the control loop includes time integration ofdata 1445 from the agility agents 1411, spatial integration of data 1444from the agility agents 1411, and fusion 1430 with data from othersources and user input 1435 to make an operating channel selection 1455for agility agent 1411. As shown, the control loop also may includebuffers 1447, 1449 (temporal), 1443 (spatial), 1446 (temporal) andfilters 1448 as needed. The other agility agents 1410 may also havetheir own control loops similar to that illustrated in FIG. 14.

As previously discussed, the agility agent transmits information to thecloud intelligence engine including information about the detected radarpattern including signal strength, type of radar, and a time stamp forthe detection. The type of radar detected includes information such asburst duration, number of bursts, pulses per burst, burst period, scanpattern, pulse repetition rate and interval, pulse width, chirp width,beam width, scan rate, pulse rise and fall times, frequency modulation,frequency hopping rate, hopping sequence length, and pulses per hop. Thecloud intelligence engine uses this information to improve its falsedetection algorithms. For example, if an agility agent detects aparticular radar type that it knows cannot be present in a certainlocation, the cloud intelligence engine can use that information in itprobability algorithm for assessing the validity of that signal. Theagility agent may transmit information to the cloud intelligence enginevia an access point or via a client device as shown in FIG. 2.

Because the cloud intelligence engine has location information for theattached radar sensors, when the cloud intelligence engine receives aradar detection signal from one sensor, the cloud intelligence enginemay use the location information for that sensor to verify the signal.The cloud intelligence engine may determine nearby sensors in thevicinity of the first sensor that detected the radar signal and searchfor the whitelist/blacklist channel history in the other sensors, and ifthe nearby sensors have current and sufficient information, the cloudintelligence engine may validate or invalidate the original radardetection from the first sensor.

Alternatively, the cloud intelligence engine or the first sensor mayinstruct nearby sensors (either through the cloud or locally) to focuson the detected channel and report their whitelist and blacklist back tothe cloud. If the nearby sensors have current and sufficientinformation, the cloud intelligence engine may validate or invalidatethe original radar detection from the first sensor. Further, based onthe location information for the first sensor, the cloud intelligenceengine may direct other nearby sensors to modify their scan times orcharacteristics or signal processing to better detect the signaldetected by the first sensor.

FIGS. 15A and 15B illustrates the logical interface between the wirelessagility agent, the cloud intelligence engine, and an access point (orsimilarly a small cell LTE-U base station). In particular this figureillustrates examples of the signaling and messages that can be exchangedbetween the agility agent and the cloud intelligence engine, and betweenthe cloud intelligence engine and an access point (via the agilityagent) during the phases of DFS scan operations, In-Service Monitoring(ISM) and when a radar event occurs forcing a channel change.

FIG. 15A illustrates an interface between the cloud intelligence engine235, the agility agent 200 and the host access point 218. For example,signaling and/or messages may be exchanged between the cloudintelligence engine 235 and the agility agent 200. The signaling and/ormessages between the cloud intelligence engine 235 and the agility agent200 may be exchanged during a DFS scan operation, during an ISMoperation and/or when a radar event occurs that results in changing of aradio channel. In an aspect, the signaling and/or messages between thecloud intelligence engine 235 and the agility agent 200 may be exchangedvia a WAN (e.g., WAN 234) and/or a secure communication tunnel.

An authentication registration process 1502 of the cloud intelligenceengine 235 may be associated with a message A. The message A may beexchanged between the cloud intelligence engine 235 and the agilityagent 200. Furthermore, the message A may be associated with one or moresignaling operations and/or one or more messages. The message A mayfacilitate an initialization and/or authentication of the agility agent200. For example, the message may include information associated withthe agility agent 200 such as, but not limited to, a unit identity, acertification associated with the agility agent 200, a nearest neighborsscan list associated with a set of other agility agents within a certaindistance from the agility agent 200, service set identifiers, a receivedsignal strength indicator associated with the agility agent 200 and/orthe host access point 218, a maker identification associated with thehost access point 218, a measured location (e.g., a global positioningsystem location) associated with the agility agent 200 and/or the hostaccess point 218, a derived location associated with the agility agent200 and/or the host access point 218 (e.g., derived via a nearby AP or anearby client), time information, current channel information, statusinformation and/or other information associated with the agility agent200 and/or the host access point 218. In one example, the message A canbe associated with a channel availability check phase.

A data fusion process 1504 of the cloud intelligence engine 235 mayfacilitate computation of a location associated with the agility agent200 and/or the host access point 218. Additionally or alternatively, thedata fusion process 1504 of the cloud intelligence engine 235 mayfacilitate computation of a set of DFS channel lists. The data fusionprocess 1504 may be associated with a message B and/or a message C. Themessage B and/or the message C may be exchanged between the cloudintelligence engine 235 and the agility agent 200. Furthermore, themessage B and/or the message C may be associated with one or moresignaling operations and/or one or more messages. The message B may beassociated with spectral measurement and/or environmental measurementsassociated with the agility agent 200. For example, the message B mayinclude information such as, but not limited to, a scanned DFS whitelist, a scanned DFS black list, scan measurements, scan statistics,congestion information, traffic count information, time information,status information and/or other measurement information associated withthe agility agent 200. The message C may be associated with anauthorized DFS, DFS lists and/or channel change. For example, themessage C may include information such as, but not limited to, adirected (e.g., approved) DFS white list, a directed (e.g., approved)DFS black list, a current time, a list valid time, a computed locationassociated with the agility agent 200 and/or the host access point 218,a network heartbeat and/or other information associated with a channeland/or a dynamic frequency selection.

A network optimization process 1506 of the cloud intelligence engine 235may facilitate optimization of a network topology associated with theagility agent 200. The network optimization process 1506 may beassociated with a message D. The message D may be exchanged between thecloud intelligence engine 235 and the agility agent 200. Furthermore,the message D may be associated with one or more signaling operationsand/or one or more messages. The message D may be associated with achange in a radio channel. For example, the message D may be associatedwith a radio channel for the host access point 218 in communication withthe agility agent 200. The message D can include information such as,but not limited to, a radio channel (e.g., a command to switch to aparticular radio channel), a valid time of a list, a network heartbeatand/or other information for optimizing a network topology.

A network update process 1508 of the cloud intelligence engine 235 mayfacilitate an update for a network topology associated with the agilityagent 200. The network update process 1508 may be associated with amessage E. The message E may be exchanged between the cloud intelligenceengine 235 and the agility agent 200. Furthermore, the message E may beassociated with one or more signaling operations and/or one or moremessages. The message E may be associated with a network heartbeatand/or a DFS authorization. For example, the message E may includeinformation such as, but not limited to, a nearest neighbors scan listassociated with a set of other agility agents within a certain distancefrom the agility agent 200, service set identifiers, a received signalstrength indicator associated with the agility agent 200 and/or the hostaccess point 218, a maker identification associated with the host accesspoint 218, a measured location update (e.g., a global positioning systemlocation update) associated with the agility agent 200 and/or the hostaccess point 218, a derived location update (e.g., derived via a nearbyAP or a nearby client) associated with the agility agent 200 and/or thehost access point 218, time information, current channel information,status information and/or other information. In one example, the messageB, the message C, the message D and/or the message E can be associatedwith an ISM phase.

A manage DFS lists process 1510 of the agility agent 200 may facilitatestorage and/or updates of DFS lists. The manage DFS lists process 1510may be associated with a message F. The message F may be exchangedbetween the agility agent 200 and the host access point 218. In oneexample, the message F may be exchanged via a local area network (e.g.,a wired local area network and/or a wireless local area network).Furthermore, the message F may be associated with one or more signalingoperations and/or one or more messages. The message F may facilitate achange in a radio channel for the host access point 218. For example,the message F may include information such as, but not limited to, anearest neighbors scan list associated with a set of other agilityagents within a certain distance from the agility agent 200, service setidentifiers, a received signal strength indicator associated with theagility agent 200 and/or the host access point 218, a makeridentification associated with the host access point 218, a measuredlocation update (e.g., a global positioning system location update)associated with the agility agent 200 and/or the host access point 218,a derived location update (e.g., derived via a nearby AP or a nearbyclient) associated with the agility agent 200 and/or the host accesspoint 218, time information, current channel information, statusinformation and/or other information. In one example, the message F maybe associated with a cloud directed operation (e.g., a cloud directedoperation where DFS channels are enabled).

FIG. 15B also illustrates an interface between the cloud intelligenceengine 235, the agility agent 200 and the host access point 218. Forexample, FIG. 15B may provide further details in connection with FIG.15A. As shown in FIG. 15B, signaling and/or messages may be exchangedbetween the cloud intelligence engine 235 and the agility agent 200. Thesignaling and/or messages between the cloud intelligence engine 235 andthe agility agent 200 may be exchanged during a DFS scan operation,during ISM and/or when a radar event occurs that results in changing ofa radio channel. In an aspect, the signaling and/or messages between thecloud intelligence engine 235 and the agility agent 200 may be exchangedvia a WAN (e.g., WAN 234) and/or a secure communication tunnel.

As also shown in FIG. 15B, the network update process 1508 of the cloudintelligence engine 235 may facilitate an update for a network topologyassociated with the agility agent 200. The network update process 1508may be associated with the message E. Then, a DFS list update process1514 of the cloud intelligence engine 235 may facilitate an update toone or more DFS channel lists. The DFS list update process 1514 may beassociated with a message G. The message G may be exchanged between thecloud intelligence engine 235 and the agility agent 200. In one example,the message G may be exchanged via a WAN (e.g., WAN 234) and/or a securecommunication tunnel. Furthermore, the message G may be associated withone or more signaling operations and/or one or more messages. Themessage G may be associated with a radar event. For example, the messageG may signal a radar event. Additionally or alternatively, the message Gmay include information associated with a radar event. For example, themessage G may include information such as, but not limited to, a radarmeasurement channel, a radar measurement pattern, a time associated witha radar event, a status associated with a radar event, other informationassociated with a radar event, etc. The radar event may associated withone or more channels from a plurality of 5 GHz communication channels(e.g., a plurality of 5 GHz communication channels associated with the 5GHz Wi-Fi spectrum 101). In one example, the message G can be associatedwith an ISM phase. The DFS list update process 1514 may also beassociated with the message C.

Moreover, as also shown in FIG. 15B, the manage DFS lists process 1510may be associated with the message F. The message F may be exchangedbetween the agility agent 200 and the host access point 218. A radardetection process 1516 of the agility agent 200 may detect and/orgenerate the radar event. Additionally, the radar detection process 1516may notify the host access point 218 to change a radio channel (e.g.,switch to an alternate radio channel). The message F and/or a manage DFSlists process 1512 may be updated accordingly in response to the changein the radio channel. In an aspect, signaling and/or messages may beexchanged between the cloud intelligence engine 235 and the host accesspoint 218 during a DFS scan operation, during an ISM operation and/orwhen a radar event occurs that results in changing of a radio channelfor the host access point 218.

In addition to traditional infrastructure network topologies (e.g., hostAccess point and clients and peer-to-peer networks or Wi-Fi-Direct), thepresent disclosures apply to extended infrastructure network topologies(e.g., mesh networks). For example, the host access points discussedherein could be a mesh peer participating in a mesh network andsimultaneously providing infrastructure connectivity.

FIG. 16A illustrates the hidden node problem where an access points orsmall cell base station 1630 is hidden from view of other access pointsor small cell base stations 1631 by topography, obstruction, distance orchannel conditions 1645. The hidden node problem is a particularlydifficult issue with mesh networks or peer-to-peer sessions where theseaccess points are communicating with each other; the hidden node 1630may not detect the frame and would be unable to synchronize its networkallocation vector (NAV). With this impairment the hidden node 1630transmissions can potentially collide and interfere with communicationsbetween the other two nodes 1631, 1632. As shown in FIG. 16A, theagility agent 1650 reports scan lists to the cloud intelligence engine1635 but cannot detect the hidden node 1630. Accordingly, the agilityagent 1650 does not report the hidden node 1630 to the cloudintelligence engine 1635 in the reported scan lists. Agility agents 1651associated with access points 1632 in neighboring networks also reportscan lists to the cloud intelligence engine 1635. Because the hiddennode 1630 may be detected by these agility agents 1651, the reportedscan lists include the hidden node 1630. The cloud intelligence engine1635 collects scan lists, from all agility agents 1650, 1651 includinggeographic information about the agility agents 1650, 1651. The cloudintelligence engine 1635 then determines the presence of the hidden node1630 and reports the presence of the hidden node 1630 to agility agents1650, 1651.

FIG. 16B illustrates the hidden radar problem, where a radar emitter1660 is unseen by an agility agent 1653 due to topography or obstruction1655. The hidden radar problem is a very serious concern of the FCC (andother regulators) because agility agent 1653 acting as a DFS masterdevice for access points 1634 but not seeing the hidden radar 1660 maycause unintended interference. Agility agents 1652 near exposed nodes1633 detect radar from a radar emitter 1660 and report to the cloudintelligence engine 1635 via an uplink back list message for example.The cloud intelligence engine 1635 informs agility agents 1653 nearhidden nodes 1634 of the radar via a downlink black list message forexample.

In some embodiments, an agility agent may be linked to multiple hostaccess points. In one such possible configuration, a significant issuearises when the networking connection between the agility agent and anaccess point over Ethernet is long. FIG. 16C illustrates the hiddenradar problem where a radar emitter is unseen by an agility agent due todistance. Networked nodes 1690, 1691, 1692 are far from a radar emitter1675 and therefore do not detect the presence of radar signals. Thenodes 1690, 1691, 1692 communicate this information to the agility agent1670. The agility agent 1670 causes corresponding white lists and blacklists to be broadcast wirelessly and over wired connections. A hiddennode 1680 receives the lists from the agility agent 1670 but is in thepresence of radar from the radar emitter 1675. The hidden node 1680 isseparated from the agility agent 1670 by a long distance and isconnected to the agility agent by a very long Ethernet connection 1681for example.

Because the hidden node 1680 is distant from the agility agent 1670, itssignature 1682 is not on the agility agent's 1670 scan list. Also,because the hidden node 1680 is too distant from the agility agent 1670,the hidden node 1680 cannot receive the wireless white list and/or blacklist from the agility agent 1670 or the time stamps of the wirelesslists do not match those received via Ethernet when received by thehidden node 1680. To solve this problem, the white lists and/or blacklists broadcast over wired Ethernet must match with the lists and timingbroadcast over wireless in order for the node 1680 to use DFS channels.Also, the agility agent 1670 may broadcast list of authorized accesspoints (e.g., 1690, 1691, 1692), and the control agent in the accesspoint must see its SSID in the authorization list in order to use DFSchannels. The agility agent 1670 only authorizes access points (e.g.,1690, 1691, 1692) which it sees by scan list and above a certain RSSIthreshold. Access points 1680 who are not seen or have RSSI too low aredeemed too far to use the agility agent's 1670 white list.

FIG. 16A-C illustrate how a cloud intelligence engine collecting datafrom a plurality of wireless agility agents proximal to the hidden nodeor hidden radar is able to discover the said hidden node or hiddenradar. Any access point or small cell base station that is now awarethat there is a hidden node to another access point on the same channelcan now react to the node being hidden, and similarly any (and all)access points or small cells within probable range of a radar signal,even though hidden to some of the nodes, could be directly preventedfrom using a radar-occupied channel.

In one embodiment of a system using a cloud DFS super master, the cloudDFS super master receives information from a plurality of agility agentsand/or access points. Additionally, because the cloud DFS super masterprovides the DFS channel information for client devices, some agilityagents and access points will no longer need to transmit a beaconidentifying available channels. In this situation, the system using acloud DFS super master may include sensors that are radar detectors thatperform the radar-sensing function of the agility agent described hereinbut do not transmit a beacon to identify the available channels.

The cloud DFS super master may provide the DFS super master function fora region for which the cloud DFS super master has sufficientinformation. For example, if agility agents and/or radar detectingsensors are distributed with a sufficient density in a given localityand the cloud DFS super master has received enough information forenough time for the locality to determine the radar signal signature forthe locality with enough certainty to comply with FCC or otherapplicable requirements, the cloud DFS super master may provide DFSmaster services for devices located in the locality.

With a cloud DFS super master system, traditional DFS masters andagility agents can be eliminated or operate as sensors that continue todo radar detection, but do not tell client devices what channels to use.In this system, client devices do not have to look for a beacon, butinstead can query the cloud DFS super master to determine what channelsare available to use.

This cloud DFS super master systems solve several problems inherent toprior-art DFS master systems. For example, the cloud DFS super mastersystem may receive information from external sources (such astopographic and map information from GIS servers, FCC databases, NOAAdatabases, DoD databases) that the cloud DFS super master uses togeo-fence an area from DFS communications in one or more channels. Inone example, the DoD instructs the cloud DFS super master to preventcommunications in the DFS spectrum in a given area for a time period.The cloud DFS super master system would instruct client devices not touse the DFS spectrum when the devices are in that area. In anotherexample, the cloud DFS super master is programmed to receive a requestto vacate one or more 5 GHz radio channels from a priority user. Apriority user can be a radar producer that includes a system of a radarproducing entity such as an airport or military body, or the priorityuser can be a government or emergency entity that needs priority accessto the DFS spectrum. In this example, the cloud DFS super master is alsoprogrammed to transmit a message to the client devices within theaffected areas of the request instructing the client devices to vacatethe 5 GHz radio channels in response to the request from the priorityuser. Using this system, an airplane or airport could request the cloudDFS super master to block out a 5 GHz channel along its route as it istaking off. In another embodiment, the request to vacate one or more 5GHz radio channels could come from governmental, regulatory, oremergency systems. For example, an ambulance or other emergency vehiclecan send real time requests to the cloud DFS super master to block out a5 GHz channel along its route in order to optimize communications forthe emergency vehicle. Current beaconing systems cannot efficientlysolve this problem unlike the disclosed cloud DFS super master. Thecloud DFS super master can further receive and use location informationfor the priority user to dynamically change the area in which the DFSsuper master instructs devices to vacate the channel(s) requested by thepriority user. This allows the DFS super master to geo-fence a limitedarea to maximize the availability of the DFS channels to other deviceswhile still complying with the request to vacate from the priority user.

Additionally, the cloud DFS super master systems addresses currentlimitations of use of the DFS spectrum. Currently, many DFS masterdevices are private access points that only provide access to the DFSspectrum to member client devices. Accordingly, most users in the areacannot utilize the available DFS spectrum because they are not membersof the group with access to the access point acting as the DFS master.In this situation, even though the DFS spectrum is unlicensed andgenerally available to the public for use, only a select group withaccess to the private access point can use the DFS spectrum. The cloudDFS super master addresses this inefficiency by providing DFS channelavailability information directly to client devices in any area forwhich the cloud DFS super master has sufficient spectral information.

Further, the cloud DFS super master systems addresses problems withproliferation of LTE-U devices and interoperability of LTE-U devices andWi-Fi devices. LTE-U devices use the same bands as Wi-Fi devices.However, Wi-Fi devices cannot detect LTE-U devices, and LTE-U devicescannot detect Wi-Fi devices. Consequentially, signals from LTE-U andproximate Wi-Fi devices collide and interfere with each other. The cloudDFS super master can control the timing and frequencies used byconnected devices. And because the cloud DFS super master can see all ofthe client devices—including LTE-U and Wi-Fi devices—the cloud DFS supermaster can coordinate traffic to mitigate collisions for, by example,making sure that two devices in the same area are not on the samechannel. The cloud DFS super master addresses the issue of proximateLTE-U and Wi-Fi devices without a need for the LTE-U and Wi-Fi devicesto talk to each other.

Also, as discussed above, the cloud DFS super master solves the hiddennode issue. And the cloud DFS super master can coordinate traffic amongclient devices.

In one embodiment of the cloud DFS super master system, the cloud DFSsuper master is connected to an access point that receives channelselection information from the cloud DFS super master (such as awhitelist or blacklist) and transmits beacons according to the receivedchannel selection information. In this case the cloud DFS super masterstill controls the channel selection for the access point.

FIG. 17 illustrates an exemplary embodiment of the cloud DFS supermaster system 1700 in which the cloud intelligence engine 1735 operatesas a cloud DFS super master. In the system 1700, the cloud DFS supermaster 1735 is communicatively coupled to a plurality of sensors 1750,1751, 1752 that detect radar signals in the DFS band and detect wirelesstraffic information. The plurality of sensors 1750, 1751, 1752 may be inagility agents or may be standalone sensors. In one example, thestandalone sensor includes a power supply and is self-contained in anenclosure and comprises a self-contained plug-in device. The sensors'communication with the cloud DFS super master 1735 may be continuous orintermittent. The sensors transmit information about detected radarsignals, traffic information, and geo-location information for thesensor to the cloud DFS super master 1735. The cloud DFS super master1735 may also be connected to external data sources 1760 such astopographic and map information from GIS servers, FCC databases, NOAAdatabases, DoD databases. The cloud DFS super master 1735 uses theinformation from the sensors 1750, 1751, 1752 and the external databases1760 to determine available DFS channels for the areas for which thecloud DFS super master has sufficient information. Then as shown in FIG.17, client devices 1780, 1781 then connect to the cloud DFS super master1735 to request authorized DFS channels according to the location of theclient devices 1780, 1781. The client devices 1780, 1781 transmitgeo-location information to the cloud DFS super master 1735 so that thecloud DFS super master 1735 can determine the appropriate channels basedon that location information.

In one embodiment, the cloud DFS super master system is a system fordetecting radar signals and avoiding interference with the radar signalsthat includes a cloud DFS super master, a plurality of radar detectors,and at least one client device. The plurality of radar detectors (orradar sensors) are communicatively coupled to the cloud DFS super masterand programmed to scan for a radar signal in each of a plurality of 5GHz radio channels, to transmit the results of the scan for the radarsignal to the cloud DFS super master, and to transmit geo-locationinformation for each of the plurality of radar detectors to the cloudDFS super master. The client device (or client devices) iscommunicatively coupled to the cloud DFS super master and programmed totransmit geo-location information for the client device and a requestfor available 5 GHz radio channels to the cloud DFS super master. Thecloud DFS super master is programmed to receive the results of the scanfor the radar signal from each of the plurality of radar detectors, thegeo-location information for the plurality of radar detectors, thegeo-location information for the client device and the request foravailable 5 GHz radio channels and is programmed to determine one ormore 5 GHz radio channels that are free of radar signals within adistance of the client device from the results of the scan for the radarsignal from each of the plurality of radar detectors, the geo-locationinformation for the plurality of radar detectors, and the geo-locationinformation for the client device and to transmit the one or more 5 GHzradio channels that are free of radar signals within a distance of theclient device to the client device.

In another embodiment, the cloud DFS super master is programmed toreceive information from an external data source and is programmed todetermine which of the one or more 5 GHz radio channels that are free ofradar signals within a distance of the client device from theinformation from the external data source and the results of the scanfor the radar signal from each of the plurality of radar detectors, thegeo-location information for the plurality of radar detectors, and thegeo-location information for the client device. The external data sourcecan be a GIS, an FAA radar database, a DoD radar database, an FCCdatabase, or a NOAA database for example.

Along with radar detection information, the plurality of radar detectorsmay be programmed to transmit wireless spectrum information (such astraffic, congestion, channels used by proximate access points) to thecloud DFS super master and the cloud DFS super master is programmed tocoordinate transmissions of the client device. This way, the cloud DFSsuper master can coordinate traffic for several devices including accesspoints to reduce congestion and collisions from using the same channelat the same time. The cloud DFS super master may apply time divisionand/or frequency division coordination to improve the client devices'performance.

As described above, the cloud DFS super master uses the location ofradar detectors to determine areas in which a radar signal has beendetected as well as areas that are free of radar signals. With manyradar detectors distributed over a wide area, including locationdetection hardware and software in each radar detector may be costprohibitive. Also, recording the location of each radar detector mayalso be impractical.

In one example, the distributed radar detectors disclosed herein areconfigured to gather location information from proximate wirelessdevices, such as mobile phones, and transmit that information to thecloud DFS super master. The location information from the wirelessdevice is not necessarily the location of the radar detector.Accordingly, the cloud DFS super master aggregates location informationfrom many wireless devices proximate to the radar detector (or multipleradar detectors), post processes and time shifts the information, andcalculates the location of the radar detector(s) based on the locationinformation from the wireless devices.

In one example, the radar detector is programmed to query nearbydevices, such as phones, asking for their location. For example,proximate devices may be programmed with an application thatcommunicates with the radar detector via Bluetooth and transmits thedevices location. Bluetooth is just one example of the transmissionsystem that may be used. In a preferred embodiment, the communicationprotocol is a connectionless protocol. Connectionless describescommunication between two end points in which a message can be sent fromone end point to another without prior arrangement. A connectionlessprotocol can include those using “broadcast” protocols that are notre-sent and do not have an intended recipient. Rather the broadcast isperiodic, and eventually a listener passing by could pick up a messagein a beacon for example. The message could be encrypted and securelyhashed so that a passive listener is assured that the broadcast wasauthentic. Accordingly, the device at one end of the communicationtransmits data to the other, without first ensuring that the recipientis available and ready to receive the data. The device sending a messagesimply sends it addressed to the intended recipient. If there areproblems with the transmission, it may be necessary to resend the dataseveral times. Connectionless protocols are often described as statelessbecause the endpoints have no protocol-defined way to remember wherethey are in a “conversation” of message exchanges. The alternative tothe connectionless approach uses connection-oriented protocols, whichare sometimes described as stateful because they can keep track of aconversation. A connectionless protocol is advantageous to allow theclient device to send location information to one or more radardetectors proximate to the client device. In one example, a clientdevice transmits location information that is received by more than oneradar detector. The radar detectors transmit this information to thecloud DFS super master that can use the information from both radardetectors to triangulate (and/or multiangulate, trilaterate,multilaterate) locations of the radar detectors relative to the clientlocation information. The cloud DFS super master may also employ machinelearning or artificial intelligence (“AI”) to intelligently sort andweigh the location samples.

The devices include location-detecting hardware such as GPS. The devicesmay use additional sensors with GPS to assist with determining location.For example, GPS may be augmented with other sensors (e.g., to aid indead-reckoning if GPS signal is lost indoors) such as inertial sensors.The devices send their location to the radar detector. The radardetector receives multiple signals from devices all around the radardetector over time. These signals may include location information fordevices proximate to the radar detector in all three dimensions(including above and below the detector). The radar detector transmitsthis information to the cloud DFS supper master that collects thisinformation over time. The cloud DFS super master then averages,triangulates, multiangulates, trilaterates, multilaterates, uses machinelearning or AI, and/or performs other mathematical calculations todetermine the location of the radar detector. In this calculation, thecloud DFS super master may also make determinations about the value ofthe GPS data in the location calculation. For example, someone who camein from a parking garage adjacent to a building in which the radardetector is located may have outdated GPS information in their device.Whereas someone who just entered the building from an outside door willhave more up to date information. The device may send a time stamp withthe location information that the radar detector transmits to the cloudDFS super master along with a receive time stamp indicating when theradar detector received the location information. The cloud DFS supermaster compares the two time stamps and determines the value of thelocation information in determining the location of the radar detector.Because an outdated location time stamp may indicate that the deviceuser has traveled a substantial distance since the location informationwas gathered, it may not be as pertinent to the location of the radardetector. Accordingly, location information that is recent may be givena higher weight in determining the location of the radar detector.

Additionally, the radar detector may transmit other information to thecloud DFS super master including information about the signal from thedevice including signal strength, receive strength (e.g., RX, RSSI). Forexample, the cloud DFS super master may determine if the signal strengthis stronger or weaker, and thereby can determine whether the signaloriginated closer to or further away from the radar detector than othersources. Alternatively, the cloud DFS super master may determine thespecific mapping or correlation between signal strength and the locationof the specific device from the radar detector to estimate of thedistance of each signal from the radar detector.

The cloud DFS super master may also use the time stamp from the radardetector to correlate the location information to other information thecloud DFS super master has received from external databases. Forexample, the cloud DFS super master may use the time of the signal to goback in historical data and look at where the corresponding satelliteswere located at that time of day to determine how accurate thesatellites were at that time. Thereby, the cloud DFS super master maydetermine the appropriate weighting to give to the location informationreceived. Additionally, the cloud DFS super master may filter thecollected location information based on other information such assatellite ephemeris (wobble, where at) information.

FIG. 18 is an illustration of multiple radar detectors 1801-1804distributed throughout a building 1800. FIG. 18 shows the floor plan ofone floor of the building 1800, but in this example the building 1800has multiple floors that are not shown. As shown, the building 1800 hasstairs 1810, 1811 to floors above and below the floor shown. Thebuilding 1800 also has escalators 1812, 1813 and elevators 1814 tofloors above and below that shown. The building also has a main entrance1815 from outside. FIG. 19 illustrates the building 1800 of FIG. 18 withthe distributed radar detectors 1801-1804. FIG. 19 also shows aplurality of points 1910 from which the radar detector 1803 has receivedlocation information from client devices. The radar detector 1803receives the location information 1910 from client devices and transmitsthis information (including time stamps for the location information,signal strength, etc.) to the cloud DFS super master. FIG. 20 is anillustration of the location information 1910 that the cloud DFS supermaster receives from the radar detector 1803. The cloud DFS super masterdoes not know beforehand the exact location 2003 of the radar detector1803 relative to the location information 1910. The cloud DFS supermaster uses the location information 1910 (including time stamps, signalstrength, etc.) to determine the location 2003 of the radar detector1803.

For example, as shown in FIG. 21, the cloud DFS super master may use aboundary condition, such as the circle 2110 shown, and maximize thepoints of location information 1910 within the boundary 2110 and thendetermine the center or locus of the location information (e.g., as themean or median location). Additionally, the cloud DFS super master mayweigh the location information based on, for example, the time stamp. Inon example, the stairway 1810 leads to a parking garage. The devices ofpeople entering the parking garage via the stairway 1810 may havelocation information (e.g., GPS information) that is outdated and doesnot accurately reflect the current location of the user. The cloud DFSsuper master may apply a reduced weight or discard this information indetermining the location of the radar detector 1803. Similarly, thedevices of people entering from the outside entrance 1815 may haverecent location information. And devices that are able to determinelocation information inside the building 1800 may also have recentlocation information. The cloud DFS super master may positively weightthis location information in determining the location of the radardetector 1803.

Also, the cloud DFS super master may use location information receivefrom one or more of the radar detectors 1801-1804 to determine thelocation of the radar detectors. For example, the cloud DFS super mastermay determine that detector 1801 and detector 1803 are located in thesame building. And the cloud DFS super master may determine thatdetector 1801 receives location information with more recent time stampsthan detector 1803. From that information, the cloud DFS super mastermay determine detector placement information such as that detector 1801is located near an entrance and detector 1803 is located far fromentrances. Additionally, the cloud DFS super master may use trafficpatterns from the location information including the times of day thelocation information is received. And the cloud DFS super master may uselocation information in three dimensions to help determine the locationof radar detectors 1801-1804. For example, detector 1802 may receivelocation information from users leaving the elevators 1814, escalator1812, or stairway 1810 that varies in the Z direction (or height) wherethe building 1800 is shown in the X-Y plane. The cloud DFS super masteruses this information to determine that radar detector 1802 is locatednear stairs, an escalator, or elevator (or area open to multiple levels,for example).

As shown in FIG. 22, more than one radar detector may receive locationinformation 2210 from a client device. The location information points2210 shown in FIG. 22 represent information for which radar detectors1803 and 1804 received location information from the same clientdevices. The cloud DFS super master receives the location informationfrom both radar detectors 1803, 1804 and uses the location informationto determine the location of one or more of the radar detectors 1803,1804. The cloud DFS super master may use information from several radardetectors in a building 1800 as well as radar detectors in otherlocations to determine the location of a radar detector in the building1800 (or in another location).

The cloud DFS super master may also have access to external databases(including, for example, GIS, FAA, DOD, FCC, NOAA, map databasesincluding Google Maps, Apple Maps, Bing Maps, USGS) and use the locationinformation received from radar detectors in conjunction withinformation from external databases to locate the radar detectors. Forexample, as shown in FIG. 19, the cloud DFS super master may get a roughlocation for a radar detector 1803 from location information 1910. Then,the cloud DFS super master may download building information in thearea, such as building floorplan 1800. Then based on the locationinformation 1910 including traffic patterns and determinations offeatures like stairs, elevators, and escalators, the cloud DFS supermaster orients and locates the radar detector 1803 (or multiple radardetectors) with the building information (e.g., aligning the buildingfloorplan 1800). Thereby, the cloud DFS super master can more-accuratelylocate the radar detector 1803.

FIG. 23 illustrates an exemplary method 2300 of using the radardetector(s) and the coupled cloud DFS super master to determine thelocation of the radar detector(s). As shown, the radar detector scansfor a radar signal 2301. The radar detector then transmits the resultsof the scan for the radar signal to the cloud DFS super master 2310. Thecloud DFS super master receives the results of the scan for the radarsignal from the radar detector 2320 and receives geolocation informationof a client device communicatively coupled to the radar detector 2330.The cloud DFS super master integrates the client device geolocationinformation with other client device geolocation information to generateintegrated client device geolocation information 2340. The cloud DFSsuper master then determines a location for the radar detector based atleast on the integrated client device geolocation information 2350.Finally, the cloud DFS super master determines a radio channel free ofthe radar signal based at least on the location for the radar detectorand the results of the scan for the radar signal 2360.

In the present specification, the term “or” is intended to mean aninclusive “or” rather than an exclusive “or.” That is, unless specifiedotherwise, or clear from context, “X employs A or B” is intended to meanany of the natural inclusive permutations. That is, if X employs A; Xemploys B; or X employs both A and B, then “X employs A or B” issatisfied under any of the foregoing instances. Moreover, articles “a”and “an” as used in this specification and annexed drawings shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from context to be directed to a singular form.

In addition, the terms “example” and “such as” are utilized herein tomean serving as an instance or illustration. Any embodiment or designdescribed herein as an “example” or referred to in connection with a“such as” clause is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. Rather, use of the terms“example” or “such as” is intended to present concepts in a concretefashion. The terms “first,” “second,” “third,” and so forth, as used inthe claims and description, unless otherwise clear by context, is forclarity only and does not necessarily indicate or imply any order intime.

What has been described above includes examples of one or moreembodiments of the disclosure. It is, of course, not possible todescribe every conceivable combination of components or methodologiesfor purposes of describing these examples, and it can be recognized thatmany further combinations and permutations of the present embodimentsare possible. Accordingly, the embodiments disclosed and/or claimedherein are intended to embrace all such alterations, modifications andvariations that fall within the spirit and scope of the detaileddescription and the appended claims. Furthermore, to the extent that theterm “includes” is used in either the detailed description or theclaims, such term is intended to be inclusive in a manner similar to theterm “comprising” as “comprising” is interpreted when employed as atransitional word in a claim.

What is claimed is:
 1. A system comprising: a cloud Dynamic FrequencySelection (“DFS”) super master; a radar detector communicatively coupledto the cloud DFS super master and programmed to scan for a radar signaland to transmit the results of the scan for the radar signal to thecloud DFS super master; wherein the cloud DFS super master is programmedto receive the results of the scan for the radar signal from the radardetector and to receive geolocation information of a client devicecommunicatively coupled to the radar detector, integrate the clientdevice geolocation information with other client device geolocationinformation to generate integrated client device geolocationinformation, determine a location for the radar detector based at leaston the integrated client device geolocation information, and determine aradio channel free of the radar signal based at least on the locationfor the radar detector and the results of the scan for the radar signal.2. The system of claim 1 wherein the radar detector is programmed toreceive the client device geolocation information from the client deviceand to transfer the client device geolocation information to the cloudDFS super master.
 3. The system of claim 1 wherein the cloud DFS supermaster is programmed to receive the client device geolocationinformation from the client device.
 4. The system of claim 2 wherein theradar detector comprises a Bluetooth transceiver and is programmed toreceive the client device geolocation information from the client devicevia the Bluetooth transceiver.
 5. The system of claim 3 wherein theradar detector is programmed to send a location request and a radardetector identifier to the client device that causes the client deviceto transmit the client device geolocation information and the radardetector identifier to the cloud DFS super master.
 6. The system ofclaim 1 wherein the cloud DFS super master is programmed to receiveinformation from an external data source and is programmed to determinethe location of the radar detector based at least on the informationfrom the external data source and the integrated client devicegeolocation information.
 7. The system of claim 6 wherein the externaldata source is selected from the group consisting of a GeographicalInformation System (GIS), an Federal Aviation Administration (FAA) radardatabase, a Department of Defense (DOD) radar database, an FederalCommunications Commission (FCC) database, a National Oceanic andAtmospheric Administration (NOAA) database, a crowd-sourced database,and a public/safety database.
 8. The system of claim 6 wherein theexternal data source is a map database and the cloud DFS super master isprogrammed to determine the location of the radar detector based atleast on the integrated client device geolocation information a mapreceived from the map database.
 9. The system of claim 1 wherein theradar detector comprises an agility agent.
 10. The system of claim 1wherein the radar detector comprises an access point, LTE small cell orbase station, or peer to peer device.
 11. A method comprising: scanningfor a radar signal with a radar detector communicatively coupled to acloud Dynamic Frequency Selection (“DFS”) super master; transmitting theresults of the scan for the radar signal to the cloud DFS super master;receiving with the cloud DFS super master the results of the scan forthe radar signal from the radar detector; receiving with the cloud DFSsuper master geolocation information of a client device communicativelycoupled to the radar detector; integrating with the cloud DFS supermaster the client device geolocation information with other clientdevice geolocation information to generate integrated client devicegeolocation information; determining with the cloud DFS super master alocation for the radar detector based at least on the integrated clientdevice geolocation information; and determining with the cloud DFS supermaster a radio channel free of the radar signal based at least on thelocation for the radar detector and the results of the scan for theradar signal.
 12. The method of claim 11 further comprising receivingwith the radar detector the client device geolocation information fromthe client device and transferring the client device geolocationinformation to the cloud DFS super master.
 13. The method of claim 11further comprising receiving with the cloud DFS super master the clientdevice geolocation information from the client device.
 14. The method ofclaim 12 further comprising receiving with the radar detector the clientdevice geolocation information from the client device via a Bluetoothtransceiver.
 15. The method of claim 13 further comprising sending withthe radar detector a location request and a radar detector identifier tothe client device that causes the client device to transmit the clientdevice geolocation information and the radar detector identifier to thecloud DFS super master.
 16. The method of claim 11 further comprisingreceiving with the cloud DFS super master information from an externaldata source and determining the location of the radar detector based atleast on the information from the external data source and theintegrated client device geolocation information.
 17. The method ofclaim 16 wherein the external data source is selected from the groupconsisting of a Geographical Information System (GIS), an FederalAviation Administration (FAA) radar database, a Department of Defense(DOD) radar database, an Federal Communications Commission (FCC)database, a National Oceanic and Atmospheric Administration (NOAA)database, a crowd-sourced database, and a public/safety database. 18.The method of claim 16 wherein the external data source is a mapdatabase and the cloud DFS super master is programmed to determine thelocation of the radar detector based at least on the integrated clientdevice geolocation information a map received from the map database. 19.The method of claim 11 wherein the radar detector comprises an agilityagent.
 20. The method of claim 11 wherein the radar detector comprisesan access point, LTE small cell or base station, or peer to peer device.